Information about Test

EEG analysis
aimlexchange.com/search/wiki/page/EEG_analysisDynamical system Chaos theory Artificial neural network Deep learning Convolutional neural network Recurrent neural network Machine learning Artificial intelligence

Sensor fusion
aimlexchange.com/search/wiki/page/Sensor_fusionincluding: Central limit theorem Kalman filter Bayesian networks DempsterShafer Convolutional neural network Two example sensor fusion calculations are illustrated

Conference on Neural Information Processing Systems
aimlexchange.com/search/wiki/page/Conference_on_Neural_Information_Processing_Systemsin 1986 as NIPS at the annual invitationonly Snowbird Meeting on Neural Networks for Computing organized by The California Institute of Technology and

SqueezeNet
aimlexchange.com/search/wiki/page/SqueezeNetEdgar (20170302). "Introducing SqueezeDet: low power fully convolutional neural network framework for autonomous driving". The Intelligence of Information

Jürgen Schmidhuber
aimlexchange.com/search/wiki/page/J%C3%BCrgen_Schmidhuberhis postdoc Dan Ciresan also achieved dramatic speedups of convolutional neural networks (CNNs) on fast parallel computers called GPUs. An earlier CNN

Tsetlin machine
aimlexchange.com/search/wiki/page/Tsetlin_machineand more efficient primitives compared to more ordinary artificial neural networks, but while the method may be faster it has a steep drop in signaltonoise

Graphical model
aimlexchange.com/search/wiki/page/Graphical_modelMarkov models, neural networks and newer models such as variableorder Markov models can be considered special cases of Bayesian networks. Naive Bayes classifier

Bias–variance tradeoff
aimlexchange.com/search/wiki/page/Bias%E2%80%93variance_tradeoffwhen increasing the width of a neural network. This means that it is not necessary to control the size of a neural network to control variance. This does

Kernel method
aimlexchange.com/search/wiki/page/Kernel_method(SVM) in the 1990s, when the SVM was found to be competitive with neural networks on tasks such as handwriting recognition. The kernel trick avoids the

Bootstrap aggregating
aimlexchange.com/search/wiki/page/Bootstrap_aggregatingprocedures" (Breiman, 1996), which include, for example, artificial neural networks, classification and regression trees, and subset selection in linear